Automated security analytics platform with visualization agnostic selection linked portlets

ABSTRACT

Visualization agnostic selection linked portlets provide a tree from a parent to one or more children that present each portlet with its own visualization and data synchronized with a root portlet based upon related filters. Each portlet uses its visualization to display a data set derived by applying its filter in conjunction with the filters of its ancestors. Each portlet then presents data that is at most the same size as its root in a visualization adapted to the child&#39;s type and quantity of data.

CROSS REFERENCE TO RELATED APPLICATIONS

U.S. patent application Ser. No. 13/677,121, entitled “AutomatedSecurity Analytics Platform,” inventor Brian Smith, filed on same dayherewith, describes exemplary methods and systems and is incorporated byreference in its entirety.

U.S. patent application Ser. No. 13/677,139, entitled “AutomatedSecurity Analytics Platform With Pluggable Data Collection And AnalysisModules,” inventors Brian Smith and Donovan Kolbly, filed on same dayherewith, describes exemplary methods and systems and is incorporated byreference in its entirety.

U.S. patent application Ser. No. 13/677,152, entitled “AutomatedSecurity Analytics Platform With Multi-Level Representation ConversionFor Space Efficiency And Incremental Persistence,” inventor DonovanKolbly, filed on same day herewith, describes exemplary methods andsystems and is incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates in general to the field of informationsecurity, and more particularly to an automated security analyticsplatform.

2. Description of the Related Art

Information technology has made businesses, government and individualsmore efficient. Mobile networking allows end users to interact withwork, government and home resources from almost anywhere and at almostany time. To support networking resources, business and governmententerprises often employ information technology (IT) specialists whomaintain the networking resources and protect the networking resourcesand enterprise information from unauthorized access. IT specialistsemploy a variety of tools to maintain network security, such asfirewalls, intrusion prevention, anti-virus applications, spam sortingapplications, phishing protection applications, identity management,security event management, etc. . . . Unfortunately, conventionalnetwork security tools have weaknesses and vulnerabilities that cybercriminals attack and penetrate to access sensitive information.

IT specialists attempt to protect network assets from attacks withconventional network security tools and by monitoring network activityto detect and counteract attacks. For example, IT specialists collectnetwork telemetry information, such as events, flows, logs, userauthorizations and authentications. The network telemetry is stored in adatabase using conventional database servers that communicate withnetworking resources. The network telemetry is then retrieved andanalyzed to identify unauthorized network accesses and access attempts.Often, network telemetry represents a substantial amount of data thatthe IT specialists sort and process to identify potential securitythreats. The gathering and analyzing of historical network telemetry toidentify security threats enhances conventional security measures,however, the process takes time and all too often provides informationabout network security threats only after a security breach hasoccurred.

Cyber criminals have many advantages in their malicious work against ITsecurity measures. Cyber criminals mount multi-stage attacks to pursuefinancial assets, intellectual property, network telemetry control andgovernment/trade secrets. Rule-based security measures can only react toknown threats that implicate a rule. Anomaly detection systems helpdetect new types of attacks, however, also consume large amounts of datafor analysis over lengthy time periods. Thus, anomaly detection systemshave a delayed response based upon the inherent performance limitationsof relational databases to process network information with variousknown analytics. Policy-based devices, such as firewalls and identityproducts, suffer from bit-rot and configuration errors that leavevulnerabilities waiting for an attacker. Cyber criminals working againstconventional network security tools have IT specialists outnumbered andoutgunned Cyber criminals patiently tap social media or phishinginformation with sophisticated tools that enable protracted entry andexfiltration techniques. If IT specialists or enterprise employees makea misstep, leave a door ajar or unknowingly provide a copy of thenetwork house keys, cyber criminals will eventually find access tonetwork resources.

SUMMARY OF THE INVENTION

Therefore a need has arisen for a system and method which provides anautomated security analytics platform that protects networking resourcesfrom malicious attacks. In accordance with the present invention, asystem, method and machine readable medium are provided whichsubstantially reduce the disadvantages and problems associated withprevious methods and systems for protecting networking resources frommalicious attacks.

A method, system and machine readable medium of one embodiment maintainsnetwork security by sensing network telemetry information at pluralnetwork resources, communicating the network telemetry information to anactive memory, such as DRAM acting as data memory in support ofoperation of a processor, for use as inputs to network security modulesin accordance with input specifications that support logic of a logicspecification to provide an output of an output specification for eachnetwork security module. Network security is maintained by investigatingthe network telemetry information with the security modules usingnetwork telemetry information stored in active memory and neutralizingthreats to the network with security modules in response to detectingpredetermined network telemetry information in the active memory.Network security modules activate in response to storage of networktelemetry information in predetermined allocated areas of the activememory. A memory allocation module interfaced with the active memoryallocates memory areas to network security modules for activation of thenetwork security modules as network resource sensors provide networktelemetry information to the active memory. The memory allocation modulemaintains the active memory to keep network telemetry information up todate by removing older data and allocating memory based upon the usageof network telemetry information.

Another method, system and machine readable medium of one embodimentmaintains network security by distributing network security platforms toeach of plural networks having a sensor execution environment andanalysis execution environment. Network activity is monitored at eachnetwork with sensor modules running on the sensor execution environmentto store monitored network activity in memory accessible by the analysisexecution environment. Network threats are detected with one or moreanalysis modules running on an analysis execution environment byanalyzing stored network information and, in response to detecting, oneor more of the analysis modules are distributed to plug into others ofthe plural network security platforms. For example, analysis modules aredistributed as pluggable modules that execute on the analysis executionenvironments of other network security platforms. In one embodiment,analysis modules bind an executable to become part of a dataflow from asensor table so that the analysis module activates as a sensor writesnetwork telemetry information to the sensor table that is an input tothe analysis module.

Another method, system and machine readable medium of one embodimentmanages network information, such as network telemetry informationstored in an active memory, by storing the network information asobjects, accessing the objects with a security platform, selectivelyconverting less than all of the plural objects into a serialized form inthe active memory and accessing at least some of the plural objects fromthe serialized form in the active memory with the network securityplatform. Objects in the active memory are incrementallypartially-serialized in plural partially-serialized forms to reduce theamount of active memory used in storage of the objects. Thepartially-serialized forms remain in active memory for rapid retrieval,albeit somewhat slower retrieval than fully-realized objects. A memoryallocation module determines how to incrementally perform partialserialization based upon predetermined factors, such as the complexityof an object, the storage time of an object, the frequency of retrievalof an object, and other factors that weigh the cost in memory allocatedto store the object versus the cost in increased retrieval time for theobject.

Another method, system and machine readable medium of one embodimentpresents information for analysis at a display with visualizationagnostic selection linked portlet trees. A portlet presents informationas visual images at a display with a visualization component,visualization settings and a filter. By interacting through the displaywith the root portlet, a child portlet is presented having at least theparent filter and at least one unique factor relative to the rootportlet, such as a different visualization and/or different filter. Atree of portlets from a root allows an end user to drill down into datawith each child portlet having no greater amount of data than thatpresented by the parent.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts a block diagram of network resources protected by networksecurity platforms having a dataflow engine that uses pluggable networksecurity modules interfaced with an active memory to identify andneutralize threats;

FIG. 2 depicts a block diagram of a network security platform having aprocessor and active memory to run network security modules formonitoring network resources and neutralizing network threats;

FIG. 3 depicts a block diagram of a network security module having aninput specification, logic specification and output specification;

FIG. 4 depicts a block diagram of dynamic linking between securitymodules by linking output to input specifications;

FIG. 5 depicts a flow diagram of a method for performing a dataflowengine at a network security platform to maintain security of a network;

FIG. 6 depicts a block diagram of plural network security platformsdeployed at plural networks, each network security platform havingpluggable network security modules;

FIG. 7 depicts a block diagram of one example embodiment of a pluggablemodule builder for creating network security modules that will plug intoa network security platform execution environment;

FIG. 8 depicts a block diagram of one embodiment of a pluggable analysisnetwork security module executing at a processor;

FIG. 9 depicts a block diagram of one example embodiment of activememory 16 depicted as random access memory (RAM) that provides rapidaccess to network telemetry information objects;

FIG. 10 depicts a block diagram of a system for presentation of networktelemetry information with plural visualizations in selection linkedportlet trees;

FIG. 11 depicts a flow diagram of a process for drilling down intonetwork telemetry information to evaluate network threats by creating aportlet tree and defining relationships between portlets of the tree;and

FIG. 12 depicts an example of a portlet tree display.

DETAILED DESCRIPTION

Monitoring real-time stateful network telemetry information in an activememory provides real-time network threat neutralization unavailable fromconventional network security systems that rely upon database analysisto find network security threats. An active memory used in embodimentsof the present disclosure stores network telemetry information as thenetwork telemetry information is provided from network sensors, thusallowing a dataflow engine having plural pluggable network securitymodules to neutralize security threats as the security threats presentin the active memory. The dataflow engine has defined memory and logicresource allocations for pluggable network security modules so thatefficient use of memory and processing resources provide an accurate andtimely response to network threats in rapidly-deployable modules.

Pluggable modules provide investigative, collaborative and threatneutralization functions based upon network telemetry information storedin an active memory. Sensor modules monitor network activity byinterfacing with network sensors and storing monitored network activityin active memory accessible by analysis modules. Sensor modulesselectively gather network telemetry information to allocated areas ofactive memory where network security modules analyze the networktelemetry information and take action to neutralize detected networkthreats. Sensed network telemetry information includes large quantitiesof a wide variety of activity sensed by network resources. The sensednetwork telemetry information is arranged, sorted and filtered withvisualization-agnostic selection-linked portlets that rapidly presentplural visualizations of rows, columns, graphs, aggregations, parallelcoordinates or other desired views that isolate outlier activitytypically associated with malicious attacks. The active memory providesanalysis modules with live network telemetry information directly fromcollection by network resource sensors rather than stale informationstored and then retrieved from a database. The active memory networkinformation includes state information that is often lost with archivedand retrieved information, such as state information associated withprotocols and connections, so that pluggable analysis modules correlatevirtually any number of incident parameters imaginable in real time. Asa network security platform dataflow engine detects and reacts tothreats, pluggable network security modules used to detect and respondto threats are collaboratively shared with other network securityplatforms to respond to similar threats in other networks.

Referring now to FIG. 1, a block diagram depicts network resourcesprotected by network security platforms 10 having a dataflow engine 12that uses pluggable network security modules 14 interfaced with anactive memory 16 to identify and neutralize threats. The networkresources monitored and/or protected by network security platform 10include a wide variety of physical devices that communicate, process,store and use information, such as servers 18 that support clients 20local to an intranet 22, clients 24 remotely interfaced with intranet 22through Internet 26, and mobile clients 28 remotely interfaced through amobile network 30. Some network resources are deployed within Internet26 to aid in communication of information, such as routers 32 andswitches 34. Network resources include conventional network securitydevices, such as firewalls 36 that restrict access to servers 18 orclients 20, identification authenticators 38 that restrict access toinformation based upon an end user identifier authorization, andanti-phishing and anti-spam applications 40 that filter out e-mailmessages having indications of a malicious source.

Dataflow engine 12 executes over a network security platform 10 underthe management of a security client 42. For example, network securityplatform 10 is a server interfaced with intranet 22 to communicateinformation with network resources using TCP/IP and other protocols.Network security platform 10 has one or more processors that executeinstructions stored in non-persistent memory, such as dynamic randomaccess memory, and persistent memory, such hard disk drives and solidstate drives. Dataflow engine 12 provides one or more executionenvironments that support execution of network security modules, such assensor modules 44 that collect network telemetry information sensed atvarious network resources and store the network telemetry information inactive memory 16 and analysis modules 46 that analyze network telemetryinformation stored in active memory 16. In one embodiment, dataflowengine 12 is a Python-based object-oriented environment that pushesnative code of network security modules into an execution path ofprogram memory for rapid access to network telemetry information as itarrives in active memory 16. In one embodiment, active memory 16 isdynamic random access memory (DRAM) directly accessible by theprocessor(s) running sensor modules 44 and analysis modules 46. Activememory 16 stores data memory of the dataflow engine 12 executionenvironment without archiving sensed network telemetry information to adatabase format. Security client 42 is, for example, a client computerinterfaced with network security platform 10 through a web browser thatpresents network telemetry information to an information technologyspecialist for detection and neutralization of network security threats.As network security threats are identified and neutralized by a networksecurity platform 10, pluggable modules 14 for detecting and respondingto the network security threats are stored in a pluggable module library48 for transfer and use at other network security platforms that facethe same or similar threats.

Referring now to FIG. 2, a block diagram depicts a network securityplatform 10 having a processor 50 and active memory 16 to run networksecurity modules 14 for monitoring network resources and neutralizingnetwork threats. Processor 50 interfaces with a network interface 52 toretrieve network telemetry information 54 sensed at network resourcesand store the network telemetry information 54 in active memory 16. Forexample, network security modules 14 in active memory 16 include asensor module that has native code 56 executing in program memory ofprocessor 50 to retrieve sensor information from network resources andto store the sensor information in allocated portions of active memory16 as network telemetry information 54. A memory allocation module 58executing from program memory of processor 50 associates each networksecurity module 14 with an allocated area of network telemetryinformation 54 stored in active memory 16. As network telemetryinformation 54 is stored in an area allocated to a network securitymodule 14, memory allocation module 58 activates the associated networksecurity module 14 to process the information, such as by activatingnative code 56 of the associated network security module 14 to processthe network telemetry information 54. Thus, as network telemetryinformation 54 is updated, functions associated with predeterminedportions of the network telemetry information are activated, performedand then returned to an inactive state for real-time responses. Forexample, memory allocation module 58 uses a publish and subscribe methodto link network security module outputs to the inputs of other networksecurity modules. Memory allocation module 58 manages active memory 16to maintain the most relevant network telemetry information 54 in activememory 16 without exceeding storage resources. For example, activememory 16 is DRAM that memory allocation module 58 divides intoallocated areas based upon memory allocations made for each networksecurity module 14. As an allocated area becomes full, memory allocationmodule 58 discards older and/or less relevant network telemetryinformation to a database for archiving. Memory allocation module 58tracks the usage of network telemetry information 54 to re-allocatestorage resources so that more relevant information has a greaterstorage life within active memory 16.

Referring now to FIG. 3, a block diagram depicts a network securitymodule 14 having an input specification 60, logic specification 62 andoutput specification 64. For example, network security modules 14 areobjects defined to execute in a Python execution environment. In theexample embodiment depicted by FIG. 3, the security module d has aninput specification 60 defining information stored in active memory 16used as inputs for a network security function provided by logicspecification 62. Logic specification 62 performs functions on theinputs using predetermined allocations of memory m and processingresources p to generate an output defined by output specification 64.Output specification 64 stores output in predetermined allocated areasof active memory 16 that may in turn provide an input to another networksecurity module 14. In one embodiment, memory allocation module 58dynamically optimizes network security platform 10 requirements byincluding in active memory 16 only network telemetry sources ofinformation required by the aggregation of all network security module14 input specifications 60. For example, network telemetry informationfrom sources that do not fall within the aggregate of inputspecifications 60 for a network security platform 10 are discarded fromactive memory 16 to an archive of persistent memory, such as a database.Network security modules 14 are automatically linked together by theinput and output specifications to create an efficient data flowdependence graph. In one embodiment, network security platform 10processing and memory resources are optimized by only storing networktelemetry information required by network security modules 14 in use atthe network security platform 10 and by processing logic only when newinput information associated with a security module 14 in use arrives atnetwork security platform 10. By optimizing the selection of informationsources, the memory requirements and the processing cycles of eachdynamically linked network security module 14, greater numbers ofnetwork security modules 14 can run on a given set of memory andprocessor resources. Memory allocation module 58 applies the inputspecification 62 and the memory and processing allocations of the logicspecification to allocate active memory in association with networksecurity modules 14. Memory allocations are adapted by memory allocationmodule 58 to store network telemetry information optimized in accordancewith historically measured usage. Memory allocation module 58 furtheroptimizes memory utilization by assigning a time frame for maintainingnetwork telemetry information in active memory 16 so that information isremoved from active memory 16 as the information exceeds a predeterminedaging period. In addition to aging, memory pressure is considered.Memory aging primarily determines what information to remove whilememory consumption primarily determines when to remove the information.

Referring now to FIG. 4, a block diagram depicts dynamic linking betweensecurity modules 14 by linking output to input specifications. Tables inactive memory 16 store outputs of security modules 14 with a publish andsubscribe method to link the output of selected modules to the input ofselected modules. Linking security module outputs to inputs efficientlyactivates a downstream security module only when a relevant output of anupstream security module is presented as a new input. In this manner,network security modules 14 that perform analysis or threatneutralization functions remain inactive until a sensor module outputs asensed network telemetry value that maps to an input of the analysis orneutralization module. The publish and subscribe method passes theoutput value of the sensor module to subscribed analysis andneutralization modules so that analysis and neutralization functions areactivated only when relevant inputs are sensed by network securityplatform. In one alternative embodiment, security modules are executedby multiple platforms or multiple CPUs that use a shared memoryarchitecture to provide data access to each platform or CPU.Alternatively, a non-shared memory architecture may be used for somesecurity modules, such as based upon the platform or CPU that executesthe security modules. For example, in a non-shared memory architecture,messages communicate information between security modules, such as withnetwork messaging. As an example, multiple platforms might each supportone or more security modules with a shared memory on the platform whilethe security modules communicate between platforms using a non-sharedmemory architecture, such as network messaging.

In the example embodiment depicted by FIG. 4, five network securitymodules 14 have dynamically constructed links that selectively activatedownstream network security modules d2, d4 and d5 when outputs are madeby d1 and d3. Network security modules d1 and d3 receive inputs from asubset of external input sources stored in allocated areas of activememory 16. Network security module d2 has an input from d1 and modulesd4 and d5 have an input from module d3. Generally, network securityplatform 10 is a system S of n security modules d(1) through d(n) thatoptimizes data flow by selecting only the subset of all networktelemetry information data sources needed for the system S to operate.In the example embodiment depicted by FIG. 4, the optimized data flow isthe intersection of the inputs of all externally-facing inputs, whichequals the inputs i(1) to security module d(1) plus the inputs i(3) tosecurity module d(3). Active memory 16 need only store these optimizedinputs to support a network security platform dataflow engine havingnetwork security modules d(1-5), while the total memory allocated to thedataflow engine is the sum of the memory of each logic specification 62for each of the network security modules d(1-5). Each network securitymodule 14 can have very specific memory allocations so that new networksecurity modules 14 can be added to the dataflow engine incrementallywithout having to allocate large or arbitrary amounts of memory to theentire system. In an example optimized network security platform 10,only data needed or used by one or more security modules 14 are everstored in active memory 16 to optimize the use of memory and allow thenetwork security platform 10 to operate at high speed with direct accessto information stored in DRAM instead of relying upon hard disk drivestorage or archive database information. Further, limiting data saved toonly that called for by the sum of inputs of network security modules 14increases the number of network security modules that can run on a givenallocation of memory. Processing efficiency is achieved since a networksecurity module's logic processing is activated only when relevant inputchanges occur at active memory 16 that are specific to a networksecurity module so that minimal processing is performed based upon newdata events.

Referring now to FIG. 5, a flow diagram depicts a method for performinga dataflow engine at a network security platform to maintain security ofa network. At step 66, network telemetry information is sensed bysensors associated with network resource devices, such as servers,firewalls, authentication services, routers, etc. . . . At step 68, thenetwork telemetry information is communicated to an active memory of anetwork security platform. In one example embodiment, sensor modules ofthe network security platform receive the network telemetry informationand selectively store only the portions of the network telemetryinformation that fall within the sum of external inputs of networksecurity modules running on the network security platform. As anexample, network telemetry information is selectively pushed fromsensors to a predetermined active memory location with an agentassociated with the sensor. As an alternative example, network telemetryinformation is selectively pulled to an active memory location with anagent associated with the active memory platform, such as an agentrunning on a server. At step 70, the network telemetry information isstored in allocated areas of active memory by the sensor modules. Atstep 72, a new data event for an allocated area of the active memoryactivates network security modules associated with the allocated area,such as by a publish and subscribe method linking new data to networksecurity module(s) that use the new data in their input specification.At step 74, the activated network security modules perform analysis andthreat neutralization functions on the sensed information by executinglogic to use the inputs. At step 76, the active memory is maintainedwithin allocated restraints by selectively pruning information fromallocated areas based on data aging, data usage, data size or otherconstraints.

Referring now to FIG. 6, a block diagram depicts plural network securityplatforms 10 deployed at plural networks 22, each network securityplatform having pluggable network security modules 14. Pluggable networksecurity modules 14 offer rapid collaborative distribution of protectivemeasures between plural networks 22 when network threats are discoveredat one network that have the potential to spread to other networks.Pluggable functionality is provided by making network security modules14 separately installable and distributable units of software from thebase network security platform 10. Network security platform 10 is builtwith plural execution environments adapted to execute pluggable networksecurity modules 14, such as object-oriented Python executionenvironments. In the example embodiment depicted by FIG. 6, a sensorexecution environment 78 runs pluggable sensor network security modules44 that perform data mining functions by collecting selected of sensednetwork telemetry information defined by input specifications of networksecurity modules running on network security platform 10. In order tomake more rapid data transfers, serialized or partially serialized datahaving a reduced memory footprint as set forth below may be used totransfer network telemetry information instead of transferring afully-realized object form. In one example embodiment, a fully realizedobject is translated to the compact or semi-compact form of data inorder to effectuate data transfers in a more rapid manner. Additionally,an analysis execution environment 80 runs pluggable analysis networksecurity modules 46 that have logic to perform network securityfunctions with the network telemetry information. A dataflow engine isformed within a network security platform by relating one or moreexecution environments to each other. In one alternative embodiment, anetwork security platform 10 may support plural data flow engines 12,each dataflow engine 12 having plural dynamically linked pluggablemodules 14. An application programming interface defines communicationsbetween the execution environment and pluggable modules so thatpluggable modules adapt collaboratively to network security platforms 10as needed. For example, the execution environment exposes a Pythonlanguage interface so that pluggable network security modules 14interface to the execution environment by defined Python subclasses andcalls into the execution environment via inherited methods. AlthoughFIG. 6 presents an example embodiment with sensor and analysis executionenvironments, in alternative embodiments alternative executionenvironments and modules may be used, such as a separate executionenvironment and modules for neutralizing network threats by locking downnetwork resources. In one alternative embodiment, pluggable modules 14also interface with execution environments with standard and portableJSON representations that define specifications for the pluggablemodules and other components, such as tables created or used by themodules.

Referring now to FIG. 7, a block diagram depicts one example embodimentof a pluggable module builder 84 for creating network security modules14 that will plug into a network security platform 10 executionenvironment. Builder 84 is an instance of a Python class that buildsnetwork security module objects with executable code by exposingbuilding blocks to a network administrator for creating a networksecurity module 14. For example, fundamental objects exposed to anetwork security module author include rows, tables and bindings thatare collected with associated code in an identifiable unit offunctionality as a network security module 14. The rows are structuredrecords mapping names to values and conforming to a schema. The tablesare a collection of rows indexed in user-definable ways that share aschema. Tables are named objects in the network security platform.Bindings are definitions of handlers associated with tables forparticular events, a type of object that embodies a connection betweenan event on a table and an entry point into executable code. Definedevents include rowcreate, which is addition of a row to a table,rowupdate, which is updating of a row in a table, and rowdelete, whichis removal of a row from a table. Network security modules 14 built bybuilder 84 have an application programming interface 86 that interfacesthe network security module with an execution environment. For example,application programming interface 86 sets commands to interact with anexecution environment for installing an object to persistent memory,starting native code of the object to program memory by pushing it intothe execution path, stopping from program memory and uninstalling thenetwork security module 14 at the execution environment.

Referring now to FIG. 8, a block diagram depicts one embodiment of apluggable analysis network security module 46 executing at a processor50. In operation, as network telemetry information is retrieved fromsensors by sensor modules 44, sensor modules 44 store the networktelemetry information that are inputs of the sum of input specificationsfor the analysis modules 46 in active memory 16 so that analysis modules46 have relevant network telemetry information in real-time. In oneembodiment, the only network telemetry information that is stored inactive memory is the network telemetry information that falls within thesum of analysis module input specifications. Sensor modules 44 store therelevant network telemetry information in the active memory to beaccessible by the analysis execution environment 80. For example, thesensor execution environment 78 arranges for relevant network telemetryinformation from sensor modules 44 to appear in a sensor table 88 of theanalysis execution environment 80 so that analysis modules 46 can bindto sensor table 88 for rowcreate events and therefore be invoked whennew network telemetry information arrives from a sensor. In oneembodiment, sensor modules are organized as Unix subprocesses that emitto stdout newline-delimited JSON records with one line and hence onerecord per event detected by the sensor module. The subprocess protocolalso includes stderr which send a plain text when the sensor has anerror or a line comprised of a JSON structure. The non-error JSONstructure messages include a structured message, which can includeseverity information, a statistics message, which monitors performanceand event processing by a sensor, or a status message, which monitorsthe sensor has a whole and components of the sensor.

As an example, an analysis module 46 stored in active memory 16 as aPython subclass module has native code 56 pushed into the program memory90 of processor 50. When a sensor module 44 stores new network telemetryinformation to sensor table 88 with a rowcreate, a binding of analysismodule 46 to the rowcreate invokes analysis module 46 to retrieve thenew network telemetry information and perform logic of the logicspecification. As part of the logic, analysis module 46 can constructits own tables, such as append only log tables or correlation tablesthat map keys to rows, to represent an output 92 of the analysis towhich other analysis modules can bind for performing higher orderanalysis. Output 92 can, for example, include a rowcreate, rowupdate orrowdelete to a table in active memory having a publish and subscriberelationship to another analysis module 46. One example of relatedpluggable modules 14 that detect, analyze and neutralize networksecurity threats is the comparing of authentication information with anetwork resource use to detect unauthorized network access attempts. Asensor module 44 detects a VPN access by a user with authenticationinformation and stores the event to active memory 16 sensor table 88 asan input to an analysis module 46 that monitors VPN accesses. The VPNaccess analysis module 46 binds to the sensor table rowcreate toretrieve the authentication information and performs logic to check foran unauthorized access attempt, such as a retrieval of the most recentbuilding magnetic card access by the end user. The VPN access analysismodule generates an output 92 by a rowupdate to a lockdown table inactive memory 16 if the VPN access attempt occurs from a remote locationwhile the end user is in an enterprise building. A lockdown analysismodule 46 binds to the rowupdate to retrieve the end user's identifierand applies the end user's identifier to perform a rowdelete of the enduser from a VPN access table, effectively locking out the end user fromVPN access. Thus, monitoring, analysis and neutralization is performedin real time from active memory with the same set of common informationand without delay introduced by archiving and then analyzing the networktelemetry information.

Referring now to FIG. 9, a block diagram of one example embodiment ofactive memory 16 is depicted as random access memory (RAM) that providesrapid access to network telemetry information objects 54. Generally, RAMprovides greater speed of access to stored information, however, RAM ismore expensive than offline storage, such as hard disk drives. In orderto store large quantities of information with less expense, informationstored as objects in RAM is serialized to a disk representation forstorage on a hard disk drive. The serialized information is restored toobject form from the serialized hard disk drive storage when retrievedfrom the hard disk drive storage for use by processing objects, however,the transformation and movement of the information introduces delays inthe processing. Maintaining all relevant network telemetry informationin one contiguous active memory of RAM to which the processor(s) havedirect access provides real-time network monitoring, analysis and threatresponse based upon the same common set of network telemetryinformation. Serializing network telemetry objects for storage in anoff-line memory separate from the active memory introduces delays inprocessing that make real-time response to threats difficult where largequantities of network telemetry information are available.

FIG. 9 depicts a representation conversion within active memory 16 thatgradually breaks down the object serialization process for increasingstorage space within active memory 16 without actually transferringpartially-serialized network telemetry information 54 to off-linestorage. One advantage of partial-serialization is that representationsof network telemetry information become more memory-efficient withinactive memory 16 with a slightly-more expensive use ofpartially-serialized information due to slightly increased retrievaltimes. The effect of partial serialization within an active memory 16 isto increase the amount of information stored in active memory 16 with aslower retrieval of information, thus providing an overall more rapidretrieval for a greater amount of information in a given memory size.Another advantage of partial-serialization is that varying degrees ofpartial-serialization are performed incrementally and asynchronously toavoid expensive “stop and write” steps associated with system writes tooff-line memory.

Network security platform 10 stores large numbers of objects, such asAVLs or rows, in active memory 16 that is directly accessible to aprocessor supporting execution environments so that a rapid response ispossible to changes in network telemetry information as the changes aresensed. In one embodiment, active memory 16 is entirely made up of DRAMthat is interfaced with a processor to provide data memory forsupporting processor operations. In alternative embodiments, activememory may instead be a contiguous block of other types of memory thatprovide data memory directly interfaced with a processor. A largestorage capacity for active memory 16 provides depth of networktelemetry information over time and minimizes access time for networksecurity modules. Memory allocation module 58 allocates various amountsof active memory 16 to different network security modules based upondesired response priorities. Memory allocation module 58 also provides afast-restart capability for network security platform 10 by taking“snapshots” of the state of memory for use in a restart if needed.Memory allocation module 58 also archives older objects to off-linestorage as needed to manage the availability of active memory 16 for newnetwork telemetry information.

In order to balance rapid response, memory size and memory availability,memory allocation module 58 defines multi-level representations ofobjects with different space and performance tradeoffs. The lower levelslower representations minimize their impedance mismatch with therequirements of off-line hard disk drive storage. In the exampleembodiment depicted by FIG. 9, memory allocation module 58 definesmulti-level representations by separating out the serialization processfrom the persistence process to achieve more efficient in-memoryrepresentations at active memory 16 with partial serialization performedover time followed at a later time by persistence to off-line storage.

A fast representation 94 of network telemetry information provides themost rapid access and the greatest memory cost. Fast representation 94stores network telemetry information with attribute values offully-realized Python objects. Essentially, in fast representation 94,memory overhead for rapid use of network telemetry information ismaintained in fully-realized object form, such as header data used tosupport pointers that allow rapid retrieval. A semicompactrepresentation 96 maintains complex attribute values, such as dictionarysets, as fully-realized Python objects while storing simpler objects inserialized form to reduce per-object overhead, such as for IP, integerand time objects. A compact representation 98 fully serializes one ormore network telemetry information objects as a separate object withshared “context” used to interpret the serialized representation, suchas a string table for interned strings. A batched representation 100assembles together compacted objects and compresses the assembledcompacted objects into a page. A persistent representation 102 preparesthe batched representation for persistent storage by keying compressedbatched strings to page numbers. Memory allocation module 58 performstransitions between the representations incrementally based upon theamount of active memory that is available, the amount of informationstored and the relative importance for each network telemetryinformation object of a rapid retrieval. To minimize the immediate costof a snapshot, objects are incrementally pushed down the hierarchy as ascheduled snapshot approaches so that fewer objects remain in fast orcompact representations.

Memory allocation module 58 selects a representation for an object basedin part upon the increased time for retrieval of the object as partialserialization progresses. In the case of batched representation 100,objects saved with a batched representation are essentially immutable sothat the object has to be extracted in order to be modified, which addsto retrieval time. Compact representations 98 that include Pythonstrings may also be immutable. Memory allocation module 58 will defaultto a fast or semicompact representation so that most accesses andmodifications will be done to mutable objects, however, immutableobjects provide a representation that allows more efficient memory usewhile retaining relatively rapid retrieval of network telemetryinformation that is less frequently used relative to retrieval times ofoff-line storage. Where a batched representation is stored to a memorymapped file, writing of the batched file to disk is asynchronous so thatobjects in the batched representation may be preemptively stored to diskstorage for archiving if extra processing cycles are available evenwhile the batched representation remains available in active storage.Other factors considered in the selection of a representation for aparticular object include the complexity of attribute values, thefrequency of access to the object, the length of time of storage inactive memory, and the timing of snapshots for rapid system restarts.Generally, memory allocation module 58 balances system response timewith memory cost by tending to keep more complex and frequently accessedobjects as fully-realized objects while partially-serializing lesscomplex and less frequently accessed objects, although other types ofpriorities may be applied as desired.

Memory allocation module 58 manages memory use in part by discardingnetwork telemetry information from active memory in time to make surethat room exists for the storage of newly sensed network telemetryinformation. In one embodiment, discarding information from activememory is performed on a page level by deleting the oldest page andremoving or marking as deleted any objects that still point to theoldest page from the object index. In one embodiment, the age fordetermining deletion is based upon modification time of the object, andin another embodiment age is based upon access time to the object. Wheremodification time is used, page numbers are assigned in sequential orderand then the lowest numbered page is the oldest. Where access time isused, each page stores its most recent access time and then pages aredeleted explicitly in age order. In another alternative embodiment,storage volumes are created and deleted just as needed to maintainstorage space in the active memory. This provides a log-structuredstorage that provides “time travel” by very quickly restoring the stateof the network security platform to a pre-existing state at a previoustime point. Alternatively, to preserve processing cycles, rather thansaving network telemetry information to an archive after it becomesoutdated, old data is simply deleted and an archive is created off-lineby a parallel storage system interfaced with network sensors.

Referring now to FIG. 10, a block diagram depicts a system forpresentation of network telemetry information with plural visualizationsin selection linked portlet trees. Linked portlets provide apresentation of network telemetry information to describe parent andchild relationships amongst an arbitrary number of portletvisualizations, each with its own visualization settings. An end userviewing linked portlets can quickly switch visualizations of selectednetwork telemetry information to analyze network activity in real timeas network resource sensors update network telemetry information inactive memory. Each root portlet 104 constrains information presented inits child portlets 106 via a selection mechanism to provide a drilldownanalysis system in which each child portlet 106 displays a smaller dataset or different visualization than its parent. Portlets provide a toolfor visualizing relationships across different values from differentportions of data, such as separate data sources. As an example, defininglabels and operators for a portlet creates virtual columns of data withfeatures that dynamically modify data models to graph visualizations andthe results of analytics. An example of a relationship discoverable fromnetwork telemetry data presented in portlets is the impact of a virus onnetwork assets. For instance, a machine virus alert sets off an analysisof operating conditions at the machine to show an increase in machineCPU cycles around the time of the virus infection. By applying avisualization of machine operating conditions and virus alerts to detectthe impact of a virus, the label and operators for the visualization maybe captured and applied to other network telemetry data to identifyvirus infections where an alert did not issue.

Portlets 104 and 106 are each a display element that includes areference to a data source, such as network telemetry information 54, afilter set 108 and a visualization 110. Portlet module 112 responds to arequest for a root portlet presentation by obtaining from an end userthe desired filter set 108 and visualization 110 and retrieving thevisualization method for visualization 110 from visualization module114. Portlet module 112 generates a root portlet 104 by mediatingbetween the data source 54, filter set 108 and visualization 110 todisplay information at a display 116 with visualization settings appliedto the method of visualization 110. Once a root portlet 104 is presentedat display 116, an end user can modify its filter 108 and visualization110 to adjust the presentation or can generate one or more linked childportlets 106 with modifications to the filter 108 and visualization 110relative to root portlet 104. Although referred to as a root portletgenerated by application of a parent filter to root information, theroot is also considered a parent portlet with a parent filter applied toparent information. Through the parent, child and sibling relationships,a parent relative to other children which is also a child or sibling maybe dynamically defined as a root that begins a new tree for a desiredvisualization.

In one example embodiment, portlet module 112 is a pluggable module 14running on a network security platform 10 that links via tables topluggable modules 14 and other portlet modules 112 to present parent andchild portlets 104 and 106 at a security client 42 having a display 116.Visualization module 114 has plural visualizations 110 for selection byportlet module 112. Each visualization 110 includes a method fordisplaying a data set based upon specified settings, such as rows,columns, graphs, aggregations, parallel coordinates or other desiredviews that isolate outlier activity typically associated with maliciousattacks against network security. For example, a bar chart visualizationpresents a bar chart based upon visualization settings that specify thefield by which to aggregate the data. As another example, a data gridvisualization presents data groups in an order of fields specified by auser in associated visualization settings. In one example embodiment,network telemetry information 54 provides a data source which declares afield set and provides a mechanism for converting related filter setsinto related data sets. A filter set is a set of zero or more comparisonoperators relative to a particular field set, which is applied toconstrain the amount of data in data sets relating to the same fieldset. Data sets displayed by a portlet are a set of rows relative to aparticular field set with each row providing a value, known as fieldvalues, for every field in the field set. Field sets are a set of onemore fields, each declaring a name, such as a ranking, and optionallyincluding a type, such as integer data.

Visualization module 114 provides visualizations 110 so that thearchitecture of selection-linked portlets is independent of the specificvisualization in use. Presenting a portlet with a visualization isperformed with a visualization component by declaring settings relevantto the visualization and selectable by an end user, by providing amethod to generate a display presentation with the visualizationsettings, and by defining a filter set to determine the informationincluded in the portlet presentation. The portlet presentation includesan interaction with the end user to allow selection of data forpresentation. In the case of a child portlet, the end user selects asubset of the root data set found in the root portlet for presentationin the child portlet. In response to selection of the child portlet andsubset of data, a method of the child visualization 110 retrieves afilter set 108 that, when applied to the root data set results inpresentation of the subdata set desired by the end user in the childportlet 106. For example, when an end user selects a child portletinitiator 118, a selection filter set of the child portlet visualization110 is applied to the root data set to select the desired data subsetfor presentation in the child portlet initiated by child portletinitiator 118. In one example embodiment, a bar chart visualization thataggregates data as bars applies a selection filter set at the selectionof a bar to initiate a child portlet 106 for including comparisonoperators that fill out all data not represented by the selected bar. Avisualization selector 120 at each portlet 104 and 106 exposes amechanism for an end user to quickly select a different visualization110 for presenting information in the portlet, such as by switchingbetween a bar graph and a data value presentation. Settings forvisualizations 110 are persisted so that the settings are re-applied ifthe user switches back to a previously-selected visualization.

In order to perform analysis of network telemetry information, aninformation technology specialist defines selection-linked portlet treesthat visualize network threats, such as outlier activity at the networkoften associated with malicious attacks or unauthorized activity. Aselection-linked portlet tree is a set of one or more portlets arrangedinto a tree such that each portlet has zero or more children, and eachportlet except the root portlet 104 has a parent. Root portlet 104represents the top of the tree and has no parent. A portlet's ancestorsinclude the parents up the tree to the root portlet. A portlet'sdescendants include the children of the portlet to the end of the tree.An information technology specialist reviewing network telemetryinformation 54 in active memory selects relevant portions of the networktelemetry information to view by selecting a filter set andvisualization. Network threats are isolated, typically as outlierinformation, by drilling down into a data set with children portlets inconjunction with the filter sets and selections filter sets of ancestorsfor the relevant data source. Each portlet displays a data set that isat most the same size as its parent's data set but typically smallerthan the parent data set as a result of applying the parent's selectionfilter set in addition to the child's own inherent filter set.

Referring now to FIG. 11, a flow diagram depicts a process for drillingdown into network telemetry information to evaluate network threats bycreating a portlet tree and defining relationships between portlets ofthe tree. The process starts at step 122 by creating a root portlet withselection of a data source, a visualization 110, and visualizationsettings. As an example, the data set might consist of sensor data fornetwork resources of a storage facility, such as authentication andaccess requests to data stored in a storage area network. In the exampleembodiment, the root portlet presents a bar graph that aggregatesobjects in the network telemetry information that result from sensorsassociated with the storage area network. The root portlet includes oneor more child portlet initiators 118 to create child portlets. At step124, a child portlet is created from the root portlet to help identifypotential threats. The child portlet varies presentation of informationby further filtering the information with an additional filter set orpresenting the information with a different visualization than the rootportlet. In the example embodiment, the storage area network informationis further filtered to isolate failed authentication attempts or viewedwith a line graph visualization of network addresses that make failedauthentication attempts. At step 126 visualizations at the parent orchild portlets are switched to provide different views of theinformation that highlight potential network security threats. At step128, the filter set for presenting information in the root or childportlets is modified by editing the selected portlet's filter set.Editing a filter set refreshes the portlet's data set and therefore itsvisualization as well as the data set and visualizations of descendantportlets that have their filter sets reset to adapt to the parent's newfilter set. At step 130, data for a root or child portlet is selected toprovide a different visualization. For instance, one or more elementswithin a portlet's visualization is selected to isolate information ofinterest, which refreshes the portlet's selection filter to update thedata set presented by the portlet and any descendent portlets.

Referring now to FIG. 12, an example of a portlet tree display isdepicted. A portlet tree presents related portlets by breadth and depth,such as in sibling or generational relationships with each other. In theexample depicted by FIG. 12, a root portlet 104 presents a bar graph tovisualize an aggregation of information, such as network telemetryinformation and is the originating parent portlet for sibling and childportlets of the example embodiment. A child portlet initiator 118presented at root portlet 104 accepts a child portlet initiation commandto initiate a selected of three types of child portlets. Activation ofan arm of child portlet initiator 118 initiates a peer or siblingportlet 132 having a shared filter with root portlet 104 but a differentvisualization, such as a line graph showing network connections depictedby the bar graph of root portlet 104. Sharing an identical parent filterwith root portlet 104 allows peer portlet 132 to present differentvisualizations of the root data generated by the parent filter whilemaintaining presentation of the root portlet 104. The presentation ofinformation in the root portlet 104 and peer child portlet 132 stayssynchronized with each other and provides a tool for an end user tocreate multiple trees of children from the same parent filter thatpresents root portlet 104 to investigate by drilling down into differentportions of the root data through different peer child portlets 132.Activation of an opposing arm of child portlet initiator 118 initiates asubordinate child portlet 134 that inherits the filter from its rootportlet 104 and adds an additional filter set for reducing theinformation presented by root portlet 104. Subordinate portals 134 allowan end user to drill down into specific portions of the root data andpresent the drilled down subordinate data with a visualization andsettings more appropriate for a precise analysis. Activation of a leg ofchild portlet initiator 118 initiates a subordinate select child portlet136. Subordinate select portlet 136 may also be presented by selecting aportion of root portlet 104 for more précised viewing, such as byselecting a bar of the bar graph. The subordinate select child portlet136 applies the parent filter and then applies a child filter thatidentifies data desired for presentation, such as the data associatedwith the bar of a bar graph or all of the data except the dataassociated with the bar of the bar graph. In each child, updates to dataof the root 104 results in synchronization of the data presented by thechild.

In one embodiment, portlets aid in visualization and analysis oftelemetry information by relating portions of data that do not have adefined relationship. For example, a portlet visualization of a portionof data based upon a filter having a label and operand is applied toother portions of data without similar data and operand relationships bytranslating the filter of the first portlet to the use as the filter ofthe second portlet. A filter translator provides a tool for creating,deleting or modifying relationships of existing portlets to adapt avisualization of existing portlets to other data. An example of a filtertranslator is the translation of a filter for data kept by the hour touse with data kept by the minute. Other more complex filter translatorsapply a function as a filter translator that operates on data of one setto generate data comparable to that presented by the portletvisualization. In alternative embodiments, various translation filtersmay be used so that visualizations of data tracked by differentparameters provide a meaningful comparison, thus allowing repetition ofthe use of portlet tools across different data.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A method for presenting network securityinformation at a display, the method comprising: storing the networksecurity information directly from network sensors in an active memory,the network sensors applying publish and subscribe to link the output ofat least some security modules to at least other security modules thatrelate network security information to security of the network; applyinga parent filter set to the network security information to select parentinformation for presentation in a parent portlet, the presented parentinformation relating to security of a network; presenting the parentinformation in the parent portlet at the display with a selected ofplural visualizations; interacting through the display with the parentportlet to present a child portlet, the child portlet having at leastthe parent filter and at least one unique factor relative to the parentportlet, the presented child portlet relating to security of thenetwork, wherein interacting through the display with the parent portletfurther comprises: selecting at the parent portlet plural types ofinformation presented at the visualization; in response to theselecting, generating a filter for each selected type of information;and presenting each selected type of information in the child portletwith the unique factor of the information other than the selected typesbeing filtered out of the presentation; preselecting a visualization ofthe plural visualizations to use in the presenting of each selected typeof information in the child portlet; and automatically applying thepreselected visualization upon the selecting.
 2. The method of claim 1wherein interacting through the display with the parent portlet furthercomprises: locking the parent filter for use with the child portlet; andpresenting the parent information in the child portlet with the uniquefactor of a selected of the plural visualizations and a second factor,the second factor having a visualization different from thevisualization of the parent portlet.
 3. The method of claim 2 furthercomprising: altering the network security information stored in thememory; filtering the altered network security information with theparent filter set; and synchronizing the altered network securityinformation for presentation at the parent and child portlets.
 4. Themethod of claim 1 further comprising: adding at least one filter to theparent filter set for use with the child portlet; filtering the rootinformation with the added at least one filter; and presenting childportlet with the unique factor of the visualization in the child portletpresenting the parent information filtered by the at least one filter.5. The method of claim 4 wherein the parent portlet and child portlethave a common visualization.
 6. The method of claim 1 wherein thevisualization of the parent portlet comprises a bar graph having pluralbars, the selecting at the parent portlet further comprises selecting abar of the bar graph for each type of information, and presenting eachtype of information in the child portlet further comprises presentinginformation aggregated by the selected bars.
 7. The method of claim 1wherein the information comprises network telemetry information sensedat a network.
 8. A system for analyzing network telemetry informationwith a network security platform to detect network security threats, thesystem comprising: a processor operable to process the network telemetryinformation by executing the network security platform to retrieve thenetwork telemetry information from an active random access memorywithout a database structure, the network security platform relating atleast some of the network telemetry information to each other in alogical tree by binding the network telemetry information to input andoutput specifications as the network telemetry information is stored inthe active random access memory; an active random access memoryinterfaced with the processor, the memory storing the network telemetryinformation for access by the processor; a display interfaced with theprocessor for presenting the network telemetry information as visualimages; a visualization module having plural visualizations to presentnetwork telemetry information as visual images, the visualization moduleoperable to accept preselection of visualizations from the pluralvisualizations to use in presenting predetermined information and toautomatically apply the preselected visualizations upon selection ofpresentation of the predetermined information; a portlet module operableto accept a visualization selection and a filter from a user and toapply the visualization selection and filter to the network telemetryinformation to present a portlet having an image at the display, theimage representing filtered network telemetry information in theselected visualization; and a child portlet initiator associated withthe portlet and operable to accept a user input to initiate presentationof a child portlet of the presented portlet with the portlet module, thechild portlet having at least the filter and at least one unique factorrelative to the presented portlet, the child portlet having an image atthe display of the network telemetry information, the at least oneunique factor determined by end user interaction with the parent portletto select at the parent portlet plural types of information presented atthe visualization, to generate a filter for each selected type ofinformation and to present each selected type of information in thechild portlet.
 9. The system of claim 8 wherein the unique factorcomprises locking the filter and presenting the filtered networktelemetry information with a selected of the plural visualizationsdifferent from the visualization of the presented portlet.
 10. Thesystem of claim 8 wherein the portlet module is further operable to:detect one or more alterations of the network telemetry informationstored in the memory; filter the altered network telemetry informationwith the filter set; and synchronize the altered network telemetryinformation for presentation at the presented portlet and the childportlet.
 11. The system of claim 8 wherein the unique factor comprisesat least one additional filter for use with the child portlet andwherein the portlet module applies the filter and the at least oneadditional filter to determine the network telemetry informationpresented in the child portlet.
 12. The system of claim 11 whereinpresented portlet and the child portlet have a common visualization. 13.The system of claim 8 wherein the child portlet initiator comprises oneor more images in the presented portlet, the one or more imagesrepresenting a portion of the filtered network telemetry information,the child portlet initiator responding to the selection of the image byusing the portion to generate one or more additional filters, theportlet module applying the one or more additional filters to present inthe child portlet only the filtered network telemetry informationassociated with the one or more images.
 14. The system of claim 13wherein the one or more images comprise one or more aggregations offiltered network telemetry information.
 15. The system of claim 8wherein the memory is dynamic random access memory.
 16. A non-transitorymachine readable medium comprising instructions operable to execute on aprocessor to manage network telemetry information presented at a displayby: receiving network telemetry information at an active memory;applying portions of the network telemetry information to securitymodules as the network telemetry information is received in the activememory by publishing and subscribing the network telemetry informationidentified by one or more output specifications to the security modules;deleting network telemetry information from the active memory that isnot identified by the one or more output specifications; applying aparent filter set to the network telemetry information to select parentinformation for presentation in a parent portlet; presenting the parentinformation in the parent portlet at the display with a selected ofplural visualizations; and accepting an input through the display at theparent portlet to present a child portlet, the child portlet having atleast the parent filter and at least one unique factor relative to theparent portlet, the child portlet presenting an image representingnetwork telemetry information filtered from the parent portlet;preselecting a visualization of the plural visualizations to use in thepresenting of each selected type of information in the child portlet;and automatically applying the preselected visualization upon the childportlet presenting the image representing network telemetry information;wherein accepting an input through the display at the parent portlet topresent a child portlet further comprises: selecting at the parentportlet plural types of information presented at the visualization; inresponse to the selecting, generating a filter for each selected type ofinformation; and presenting each selected type of information in thechild portlet with the parent information other than the selected typesbeing filtered out of the presentation.
 17. The machine readable mediumof claim 16 wherein the unique factor comprises a lock of the parentfilter for use with the child portlet and a visualization for use withthe child portlet that differs from the visualization of the parentportlet.
 18. The machine readable medium of claim 16 wherein the inputcomprises a selection at a portion of the parent portlet and the uniquefactor comprises a filter added to the parent filter to remove networktelemetry information other than network telemetry informationassociated with the selected portion.